How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="korvo/MS-RandomMergeTest_v2-22B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("korvo/MS-RandomMergeTest_v2-22B")
model = AutoModelForCausalLM.from_pretrained("korvo/MS-RandomMergeTest_v2-22B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the della merge method using concedo/Beepo-22B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: crestf411/MS-sunfall-v0.7.0
    parameters:
      weight: 0.35
      density: 0.75
  - model: nbeerbower/Mistral-Small-Gutenberg-Doppel-22B
    parameters:
      weight: 0.25
      density: 0.55
  - model: TheDrummer/Cydonia-22B-v1.1
    parameters:
      weight: 0.25
      density: 0.45
  - model: anthracite-org/magnum-v4-22b
    parameters:
      weight: 0.15
      density: 0.45
merge_method: della
base_model: concedo/Beepo-22B
parameters:
  int8_mask: true
  normalize: true
  epsilon: 0.05
  lambda: 1.0
dtype: bfloat16
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